EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks

TL;DR

This paper introduces a novel energy-aware mobility management scheme for mobile edge computing in ultra-dense networks, optimizing delay and energy consumption without future system information, and handling dynamic BS states.

Contribution

It develops an online, user-centric mobility management algorithm based on Lyapunov and bandit theories, considering migration costs and BS on/off dynamics, with proven bounded performance deviations.

Findings

Achieves near-optimal delay performance

Satisfies user energy constraints

Handles dynamic BS on/off states

Abstract

Merging mobile edge computing (MEC) functionality with the dense deployment of base stations (BSs) provides enormous benefits such as a real proximity, low latency access to computing resources. However, the envisioned integration creates many new challenges, among which mobility management (MM) is a critical one. Simply applying existing radio access oriented MM schemes leads to poor performance mainly due to the co-provisioning of radio access and computing services of the MEC-enabled BSs. In this paper, we develop a novel user-centric energy-aware mobility management (EMM) scheme, in order to optimize the delay due to both radio access and computation, under the long-term energy consumption constraint of the user. Based on Lyapunov optimization and multi-armed bandit theories, EMM works in an online fashion without future system state information, and effectively handles the imperfect system state information. Theoretical analysis explicitly takes radio handover and computation migration cost into consideration and proves a bounded deviation on both the delay performance and energy consumption compared to the oracle solution with exact and complete future system information. The proposed algorithm also effectively handles the scenario in which candidate BSs randomly switch on/off during the offloading process of a task. Simulations show that the proposed algorithms can achieve close-to-optimal delay performance while satisfying the user energy consumption constraint.